Getting Started with Fastreid
Prepare pretrained model
If you use backbones supported by fastreid, you do not need to do anything. It will automatically download the pre-train models.
But if your network is not connected, you can download pre-train models manually and put it in ~/.cache/torch/checkpoints.
If you want to use other pre-train models, such as MoCo pre-train, you can download by yourself and set the pre-train model path in configs/Base-bagtricks.yml.
Compile with cython to accelerate evalution
cd fastreid/evaluation/rank_cylib; make all
Training & Evaluation in Command Line
We provide a script in "tools/train_net.py", that is made to train all the configs provided in fastreid. You may want to use it as a reference to write your own training script.
To train a model with "train_net.py", first setup up the corresponding datasets following datasets/README.md, then run:
python3 tools/train_net.py --config-file ./configs/Market1501/bagtricks_R50.yml MODEL.DEVICE "cuda:0"
The configs are made for 1-GPU training.
If you want to train model with 4 GPUs, you can run:
python3 tools/train_net.py --config-file ./configs/Market1501/bagtricks_R50.yml --num-gpus 4
If you want to train model with multiple machines, you can run:
# machine 1
export GLOO_SOCKET_IFNAME=eth0
export NCCL_SOCKET_IFNAME=eth0
python3 tools/train_net.py --config-file configs/Market1501/bagtricks_R50.yml \
--num-gpus 4 --num-machines 2 --machine-rank 0 --dist-url tcp://ip:port
# machine 2
export GLOO_SOCKET_IFNAME=eth0
export NCCL_SOCKET_IFNAME=eth0
python3 tools/train_net.py --config-file configs/Market1501/bagtricks_R50.yml \
--num-gpus 4 --num-machines 2 --machine-rank 1 --dist-url tcp://ip:port
Make sure the dataset path and code are the same in different machines, and machines can communicate with each other.
To evaluate a model's performance, use
python3 tools/train_net.py --config-file ./configs/Market1501/bagtricks_R50.yml --eval-only \
MODEL.WEIGHTS /path/to/checkpoint_file MODEL.DEVICE "cuda:0"
For more options, see python3 tools/train_net.py -h.